This invention relates to scoring of acoustically-based events in a word spotting system.
Word spotting systems are used to detect the presence of specified keywords or phases or other linguistic events in an acoustically-based signal. Many word spotting systems provide a score associated with each detection. Such scores can be useful for characterizing which detections are more likely to correspond to a true events (“hits”) rather than misses, which are sometimes referred to as false alarms.
Some word spotting systems make use of statistical models, such as Hidden Markov Models (HMMs), which are trained based on a training corpus of speech. In such systems, probabilistically motivated scores have been used to characterize the detections. One such score is a posterior probability (or equivalently a logarithm of the posterior probability) that occurred (e.g., started, ended) at a particular time given acoustically-based signal and the HMM model for the keyword of interest and for other speech.
It has been observed that the probabilistically motivated scores can be variable, depending on factors such as the audio conditions and the specific word or phrase that is being detected. For example, scores obtained in different audio conditions or for different words and phrases are not necessarily comparable.